Overview

Dataset statistics

Number of variables20
Number of observations15000
Missing cells0
Missing cells (%)0.0%
Duplicate rows188
Duplicate rows (%)1.3%
Total size in memory2.3 MiB
Average record size in memory160.0 B

Variable types

NUM13
BOOL6
CAT1

Reproduction

Analysis started2020-08-24 23:53:25.134012
Analysis finished2020-08-24 23:53:53.929904
Duration28.8 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

f41 has constant value "0.0" Constant
f44 has constant value "0.0" Constant
f38 has constant value "0.0" Constant
f4 has constant value "100.0" Constant
f40 has constant value "0.0" Constant
f34 has constant value "0.0" Constant
f46 has constant value "0.0" Constant
Dataset has 188 (1.3%) duplicate rows Duplicates
f28 has 13805 (92.0%) zeros Zeros
f27 has 13856 (92.4%) zeros Zeros
f25 has 13747 (91.6%) zeros Zeros
f13 has 10180 (67.9%) zeros Zeros
f20 has 12108 (80.7%) zeros Zeros
f26 has 13870 (92.5%) zeros Zeros
f14 has 6577 (43.8%) zeros Zeros
f18 has 8128 (54.2%) zeros Zeros
f16 has 6444 (43.0%) zeros Zeros
target has 9342 (62.3%) zeros Zeros

Variables

f28
Real number (ℝ≥0)

ZEROS

Distinct count64
Unique (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0061333333333333
Minimum0.0
Maximum74.0
Zeros13805
Zeros (%)92.0%
Memory size117.3 KiB
2020-08-24T23:53:53.973648image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum74
Range74
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.687540674
Coefficient of variation (CV)5.65286974
Kurtosis62.71188986
Mean1.006133333
Median Absolute Deviation (MAD)0
Skewness7.504388342
Sum15092
Variance32.34811892
2020-08-24T23:53:54.092278image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01380592.0%
 
12871.9%
 
21651.1%
 
4760.5%
 
3740.5%
 
5540.4%
 
6310.2%
 
7280.2%
 
8270.2%
 
11200.1%
 
10200.1%
 
9180.1%
 
15170.1%
 
13160.1%
 
14150.1%
 
16150.1%
 
20140.1%
 
17140.1%
 
37140.1%
 
35140.1%
 
21120.1%
 
18120.1%
 
33110.1%
 
28110.1%
 
36110.1%
 
Other values (39)2191.5%
 
ValueCountFrequency (%) 
01380592.0%
 
12871.9%
 
21651.1%
 
3740.5%
 
4760.5%
 
5540.4%
 
6310.2%
 
7280.2%
 
8270.2%
 
9180.1%
 
ValueCountFrequency (%) 
741< 0.1%
 
665< 0.1%
 
6590.1%
 
633< 0.1%
 
6280.1%
 
611< 0.1%
 
607< 0.1%
 
581< 0.1%
 
576< 0.1%
 
566< 0.1%
 

f41
Boolean

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
0
15000
ValueCountFrequency (%) 
015000100.0%
 

f27
Real number (ℝ≥0)

ZEROS

Distinct count65
Unique (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9224
Minimum0.0
Maximum96.0
Zeros13856
Zeros (%)92.4%
Memory size117.3 KiB
2020-08-24T23:53:54.214749image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum96
Range96
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.310692843
Coefficient of variation (CV)5.757472726
Kurtosis69.4396088
Mean0.9224
Median Absolute Deviation (MAD)0
Skewness7.781885681
Sum13836
Variance28.20345847
2020-08-24T23:53:54.329083image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01385692.4%
 
12671.8%
 
21711.1%
 
3780.5%
 
4630.4%
 
5520.3%
 
7330.2%
 
8310.2%
 
6270.2%
 
14190.1%
 
16190.1%
 
15170.1%
 
12160.1%
 
20160.1%
 
32150.1%
 
10150.1%
 
13150.1%
 
11140.1%
 
17130.1%
 
22130.1%
 
26120.1%
 
9110.1%
 
33110.1%
 
29110.1%
 
37110.1%
 
Other values (40)1941.3%
 
ValueCountFrequency (%) 
01385692.4%
 
12671.8%
 
21711.1%
 
3780.5%
 
4630.4%
 
5520.3%
 
6270.2%
 
7330.2%
 
8310.2%
 
9110.1%
 
ValueCountFrequency (%) 
961< 0.1%
 
681< 0.1%
 
664< 0.1%
 
652< 0.1%
 
632< 0.1%
 
613< 0.1%
 
607< 0.1%
 
594< 0.1%
 
582< 0.1%
 
573< 0.1%
 

f44
Boolean

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
0
15000
ValueCountFrequency (%) 
015000100.0%
 

f25
Real number (ℝ≥0)

ZEROS

Distinct count68
Unique (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2483333333333333
Minimum0.0
Maximum72.0
Zeros13747
Zeros (%)91.6%
Memory size117.3 KiB
2020-08-24T23:53:54.441980image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum72
Range72
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.332940678
Coefficient of variation (CV)5.073116698
Kurtosis42.71691151
Mean1.248333333
Median Absolute Deviation (MAD)0
Skewness6.274238975
Sum18725
Variance40.10613763
2020-08-24T23:53:54.560899image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01374791.6%
 
12851.9%
 
21451.0%
 
3700.5%
 
4580.4%
 
5420.3%
 
8340.2%
 
14270.2%
 
6250.2%
 
17230.2%
 
38200.1%
 
22190.1%
 
15180.1%
 
20180.1%
 
9180.1%
 
16170.1%
 
13170.1%
 
33170.1%
 
31170.1%
 
18160.1%
 
7160.1%
 
11150.1%
 
28140.1%
 
12140.1%
 
36140.1%
 
Other values (43)2942.0%
 
ValueCountFrequency (%) 
01374791.6%
 
12851.9%
 
21451.0%
 
3700.5%
 
4580.4%
 
5420.3%
 
6250.2%
 
7160.1%
 
8340.2%
 
9180.1%
 
ValueCountFrequency (%) 
721< 0.1%
 
711< 0.1%
 
701< 0.1%
 
682< 0.1%
 
664< 0.1%
 
652< 0.1%
 
632< 0.1%
 
622< 0.1%
 
602< 0.1%
 
591< 0.1%
 

f38
Boolean

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
0
15000
ValueCountFrequency (%) 
015000100.0%
 

f13
Real number (ℝ≥0)

ZEROS

Distinct count97
Unique (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.863866666666667
Minimum0.0
Maximum134.0
Zeros10180
Zeros (%)67.9%
Memory size117.3 KiB
2020-08-24T23:53:54.683417image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile42
Maximum134
Range134
Interquartile range (IQR)2

Descriptive statistics

Standard deviation14.23226984
Coefficient of variation (CV)2.427113482
Kurtosis9.150814582
Mean5.863866667
Median Absolute Deviation (MAD)0
Skewness2.940387049
Sum87958
Variance202.5575049
2020-08-24T23:53:54.794057image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01018067.9%
 
18715.8%
 
26784.5%
 
32551.7%
 
52211.5%
 
371741.2%
 
61731.2%
 
191360.9%
 
321350.9%
 
91160.8%
 
141080.7%
 
121040.7%
 
421030.7%
 
44950.6%
 
50950.6%
 
23950.6%
 
25910.6%
 
28900.6%
 
31900.6%
 
4880.6%
 
18830.6%
 
56810.5%
 
8670.4%
 
46590.4%
 
16490.3%
 
Other values (72)7635.1%
 
ValueCountFrequency (%) 
01018067.9%
 
18715.8%
 
26784.5%
 
32551.7%
 
4880.6%
 
52211.5%
 
61731.2%
 
7450.3%
 
8670.4%
 
91160.8%
 
ValueCountFrequency (%) 
1341< 0.1%
 
1271< 0.1%
 
1133< 0.1%
 
1061< 0.1%
 
1051< 0.1%
 
1011< 0.1%
 
1002< 0.1%
 
992< 0.1%
 
981< 0.1%
 
971< 0.1%
 

f20
Real number (ℝ≥0)

ZEROS

Distinct count86
Unique (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.884533333333333
Minimum0.0
Maximum117.0
Zeros12108
Zeros (%)80.7%
Memory size117.3 KiB
2020-08-24T23:53:54.915803image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile32
Maximum117
Range117
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.42552276
Coefficient of variation (CV)2.941285806
Kurtosis12.99798676
Mean3.884533333
Median Absolute Deviation (MAD)0
Skewness3.50693657
Sum58268
Variance130.5425703
2020-08-24T23:53:55.029214image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01210880.7%
 
13482.3%
 
22651.8%
 
31451.0%
 
41050.7%
 
61020.7%
 
5980.7%
 
15600.4%
 
31550.4%
 
26540.4%
 
7520.3%
 
19510.3%
 
16500.3%
 
43490.3%
 
21490.3%
 
10490.3%
 
41480.3%
 
30460.3%
 
24450.3%
 
11440.3%
 
12430.3%
 
34410.3%
 
39410.3%
 
35400.3%
 
8400.3%
 
Other values (61)9726.5%
 
ValueCountFrequency (%) 
01210880.7%
 
13482.3%
 
22651.8%
 
31451.0%
 
41050.7%
 
5980.7%
 
61020.7%
 
7520.3%
 
8400.3%
 
9390.3%
 
ValueCountFrequency (%) 
1171< 0.1%
 
981< 0.1%
 
911< 0.1%
 
871< 0.1%
 
851< 0.1%
 
831< 0.1%
 
792< 0.1%
 
781< 0.1%
 
771< 0.1%
 
763< 0.1%
 

f5
Real number (ℝ≥0)

Distinct count184
Unique (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.8792
Minimum13.0
Maximum200.0
Zeros0
Zeros (%)0.0%
Memory size117.3 KiB
2020-08-24T23:53:55.144459image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile18
Q156
median74
Q3100
95-th percentile136
Maximum200
Range187
Interquartile range (IQR)44

Descriptive statistics

Standard deviation35.25282388
Coefficient of variation (CV)0.4585482664
Kurtosis-0.07206687396
Mean76.8792
Median Absolute Deviation (MAD)22
Skewness0.3087529885
Sum1153188
Variance1242.761591
2020-08-24T23:53:55.252727image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
745463.6%
 
594733.2%
 
564022.7%
 
763262.2%
 
713232.2%
 
612881.9%
 
182581.7%
 
772401.6%
 
1172281.5%
 
162261.5%
 
722251.5%
 
1152221.5%
 
751961.3%
 
621901.3%
 
871851.2%
 
991831.2%
 
581811.2%
 
1301791.2%
 
131791.2%
 
1141751.2%
 
801741.2%
 
791681.1%
 
571681.1%
 
641651.1%
 
921651.1%
 
Other values (159)893559.6%
 
ValueCountFrequency (%) 
131791.2%
 
14650.4%
 
151511.0%
 
162261.5%
 
17980.7%
 
182581.7%
 
19910.6%
 
20900.6%
 
21740.5%
 
221060.7%
 
ValueCountFrequency (%) 
200180.1%
 
1991< 0.1%
 
1971< 0.1%
 
1962< 0.1%
 
1941< 0.1%
 
1933< 0.1%
 
1921< 0.1%
 
1913< 0.1%
 
1903< 0.1%
 
189100.1%
 

f26
Real number (ℝ≥0)

ZEROS

Distinct count68
Unique (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.941
Minimum0.0
Maximum108.0
Zeros13870
Zeros (%)92.5%
Memory size117.3 KiB
2020-08-24T23:53:55.657050image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum108
Range108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.494718537
Coefficient of variation (CV)5.839233301
Kurtosis71.04616317
Mean0.941
Median Absolute Deviation (MAD)0
Skewness7.848822843
Sum14115
Variance30.1919318
2020-08-24T23:53:55.771337image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01387092.5%
 
12871.9%
 
21521.0%
 
4650.4%
 
3630.4%
 
6450.3%
 
5440.3%
 
7290.2%
 
11260.2%
 
8220.1%
 
9190.1%
 
17170.1%
 
16160.1%
 
14160.1%
 
36150.1%
 
15150.1%
 
39130.1%
 
10130.1%
 
12120.1%
 
21110.1%
 
25100.1%
 
24100.1%
 
23100.1%
 
2990.1%
 
3590.1%
 
Other values (43)2021.3%
 
ValueCountFrequency (%) 
01387092.5%
 
12871.9%
 
21521.0%
 
3630.4%
 
4650.4%
 
5440.3%
 
6450.3%
 
7290.2%
 
8220.1%
 
9190.1%
 
ValueCountFrequency (%) 
1081< 0.1%
 
692< 0.1%
 
673< 0.1%
 
662< 0.1%
 
653< 0.1%
 
633< 0.1%
 
623< 0.1%
 
614< 0.1%
 
601< 0.1%
 
592< 0.1%
 

f9
Real number (ℝ≥0)

Distinct count80
Unique (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.90973333333334
Minimum94.0
Maximum200.0
Zeros0
Zeros (%)0.0%
Memory size117.3 KiB
2020-08-24T23:53:55.893327image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum94
5-th percentile94
Q194
median94
Q394
95-th percentile102
Maximum200
Range106
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.55323306
Coefficient of variation (CV)0.08918003171
Kurtosis35.69945059
Mean95.90973333
Median Absolute Deviation (MAD)0
Skewness5.767064447
Sum1438646
Variance73.15779578
2020-08-24T23:53:56.001250image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
941300186.7%
 
955113.4%
 
972151.4%
 
961921.3%
 
98940.6%
 
99710.5%
 
100690.5%
 
101570.4%
 
102540.4%
 
103400.3%
 
106330.2%
 
104260.2%
 
105250.2%
 
107210.1%
 
122180.1%
 
115180.1%
 
121160.1%
 
119160.1%
 
108160.1%
 
114150.1%
 
120140.1%
 
110140.1%
 
123130.1%
 
130130.1%
 
149130.1%
 
Other values (55)4252.8%
 
ValueCountFrequency (%) 
941300186.7%
 
955113.4%
 
961921.3%
 
972151.4%
 
98940.6%
 
99710.5%
 
100690.5%
 
101570.4%
 
102540.4%
 
103400.3%
 
ValueCountFrequency (%) 
2002< 0.1%
 
1721< 0.1%
 
1713< 0.1%
 
1704< 0.1%
 
1691< 0.1%
 
1682< 0.1%
 
1671< 0.1%
 
1663< 0.1%
 
1651< 0.1%
 
1645< 0.1%
 

f4
Categorical

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
100
15000
ValueCountFrequency (%) 
10015000100.0%
 
2020-08-24T23:53:56.206273image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
04500060.0%
 
11500020.0%
 
.1500020.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number6000080.0%
 
Other Punctuation1500020.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
04500075.0%
 
11500025.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.15000100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common75000100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
04500060.0%
 
11500020.0%
 
.1500020.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII75000100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
04500060.0%
 
11500020.0%
 
.1500020.0%
 

f7
Real number (ℝ≥0)

Distinct count114
Unique (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.7676
Minimum76.0
Maximum200.0
Zeros0
Zeros (%)0.0%
Memory size117.3 KiB
2020-08-24T23:53:56.326082image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum76
5-th percentile76
Q176
median76
Q381
95-th percentile122
Maximum200
Range124
Interquartile range (IQR)5

Descriptive statistics

Standard deviation16.33012049
Coefficient of variation (CV)0.1949455456
Kurtosis7.572695946
Mean83.7676
Median Absolute Deviation (MAD)0
Skewness2.659646656
Sum1256514
Variance266.6728351
2020-08-24T23:53:56.436875image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
76897759.8%
 
778625.7%
 
785873.9%
 
794402.9%
 
802351.6%
 
812001.3%
 
821851.2%
 
831621.1%
 
841350.9%
 
851060.7%
 
90940.6%
 
99920.6%
 
87910.6%
 
86860.6%
 
89850.6%
 
88850.6%
 
96850.6%
 
92820.5%
 
100790.5%
 
91780.5%
 
95760.5%
 
98740.5%
 
94720.5%
 
97710.5%
 
104670.4%
 
Other values (89)189412.6%
 
ValueCountFrequency (%) 
76897759.8%
 
778625.7%
 
785873.9%
 
794402.9%
 
802351.6%
 
812001.3%
 
821851.2%
 
831621.1%
 
841350.9%
 
851060.7%
 
ValueCountFrequency (%) 
2003< 0.1%
 
1941< 0.1%
 
1923< 0.1%
 
1901< 0.1%
 
1891< 0.1%
 
1881< 0.1%
 
1871< 0.1%
 
1862< 0.1%
 
1852< 0.1%
 
1841< 0.1%
 

f40
Boolean

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
0
15000
ValueCountFrequency (%) 
015000100.0%
 

f34
Boolean

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
0
15000
ValueCountFrequency (%) 
015000100.0%
 

f14
Real number (ℝ≥0)

ZEROS

Distinct count117
Unique (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.251333333333333
Minimum0.0
Maximum134.0
Zeros6577
Zeros (%)43.8%
Memory size117.3 KiB
2020-08-24T23:53:56.566957image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q319
95-th percentile56
Maximum134
Range134
Interquartile range (IQR)19

Descriptive statistics

Standard deviation19.79110939
Coefficient of variation (CV)1.615424938
Kurtosis2.812277997
Mean12.25133333
Median Absolute Deviation (MAD)1
Skewness1.795890385
Sum183770
Variance391.6880108
2020-08-24T23:53:56.682759image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0657743.8%
 
112048.0%
 
29766.5%
 
35063.4%
 
52932.0%
 
372771.8%
 
322271.5%
 
62191.5%
 
192061.4%
 
422031.4%
 
41861.2%
 
91801.2%
 
441771.2%
 
141641.1%
 
121601.1%
 
281531.0%
 
501521.0%
 
231521.0%
 
561461.0%
 
251441.0%
 
311360.9%
 
181300.9%
 
81290.9%
 
71020.7%
 
46990.7%
 
Other values (92)210214.0%
 
ValueCountFrequency (%) 
0657743.8%
 
112048.0%
 
29766.5%
 
35063.4%
 
41861.2%
 
52932.0%
 
62191.5%
 
71020.7%
 
81290.9%
 
91801.2%
 
ValueCountFrequency (%) 
1341< 0.1%
 
1282< 0.1%
 
1272< 0.1%
 
1261< 0.1%
 
1191< 0.1%
 
1181< 0.1%
 
1151< 0.1%
 
1141< 0.1%
 
1131< 0.1%
 
1121< 0.1%
 

f18
Real number (ℝ≥0)

ZEROS

Distinct count123
Unique (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.862533333333333
Minimum0.0
Maximum143.0
Zeros8128
Zeros (%)54.2%
Memory size117.3 KiB
2020-08-24T23:53:56.800206image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q319
95-th percentile54
Maximum143
Range143
Interquartile range (IQR)19

Descriptive statistics

Standard deviation19.61403112
Coefficient of variation (CV)1.65344371
Kurtosis3.030679684
Mean11.86253333
Median Absolute Deviation (MAD)0
Skewness1.810548525
Sum177938
Variance384.7102169
2020-08-24T23:53:56.916584image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0812854.2%
 
17114.7%
 
24483.0%
 
32141.4%
 
51721.1%
 
61541.0%
 
41360.9%
 
361280.9%
 
71270.8%
 
441210.8%
 
321200.8%
 
141200.8%
 
101170.8%
 
271140.8%
 
91110.7%
 
81060.7%
 
281060.7%
 
211020.7%
 
161020.7%
 
201020.7%
 
381020.7%
 
231010.7%
 
311010.7%
 
341010.7%
 
391000.7%
 
Other values (98)305620.4%
 
ValueCountFrequency (%) 
0812854.2%
 
17114.7%
 
24483.0%
 
32141.4%
 
41360.9%
 
51721.1%
 
61541.0%
 
71270.8%
 
81060.7%
 
91110.7%
 
ValueCountFrequency (%) 
1431< 0.1%
 
1351< 0.1%
 
1301< 0.1%
 
1281< 0.1%
 
1251< 0.1%
 
1191< 0.1%
 
1182< 0.1%
 
1162< 0.1%
 
1141< 0.1%
 
1132< 0.1%
 

f46
Boolean

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
0
15000
ValueCountFrequency (%) 
015000100.0%
 

f16
Real number (ℝ≥0)

ZEROS

Distinct count120
Unique (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.1406
Minimum0.0
Maximum133.0
Zeros6444
Zeros (%)43.0%
Memory size117.3 KiB
2020-08-24T23:53:57.038177image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q319
95-th percentile55.05
Maximum133
Range133
Interquartile range (IQR)19

Descriptive statistics

Standard deviation19.5274948
Coefficient of variation (CV)1.608445612
Kurtosis3.350050544
Mean12.1406
Median Absolute Deviation (MAD)1
Skewness1.862008094
Sum182109
Variance381.3230532
2020-08-24T23:53:57.143211image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0644443.0%
 
112038.0%
 
29926.6%
 
35253.5%
 
53012.0%
 
372781.9%
 
62411.6%
 
322191.5%
 
142041.4%
 
42021.3%
 
281981.3%
 
191941.3%
 
91811.2%
 
231741.2%
 
311731.2%
 
501661.1%
 
411631.1%
 
561621.1%
 
121591.1%
 
181531.0%
 
251511.0%
 
81431.0%
 
461040.7%
 
431040.7%
 
161020.7%
 
Other values (95)206413.8%
 
ValueCountFrequency (%) 
0644443.0%
 
112038.0%
 
29926.6%
 
35253.5%
 
42021.3%
 
53012.0%
 
62411.6%
 
71010.7%
 
81431.0%
 
91811.2%
 
ValueCountFrequency (%) 
1331< 0.1%
 
1282< 0.1%
 
1272< 0.1%
 
1252< 0.1%
 
1201< 0.1%
 
1191< 0.1%
 
1182< 0.1%
 
1171< 0.1%
 
1162< 0.1%
 
11280.1%
 

target
Real number (ℝ≥0)

ZEROS

Distinct count11
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.945333333333334
Minimum0.0
Maximum100.0
Zeros9342
Zeros (%)62.3%
Memory size117.3 KiB
2020-08-24T23:53:57.257383image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q370
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)70

Descriptive statistics

Standard deviation41.72581632
Coefficient of variation (CV)1.441538636
Kurtosis-0.9923344746
Mean28.94533333
Median Absolute Deviation (MAD)0
Skewness0.9176513652
Sum434180
Variance1741.043748
2020-08-24T23:53:57.359795image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0934262.3%
 
100300520.0%
 
903632.4%
 
103152.1%
 
203022.0%
 
303012.0%
 
502992.0%
 
702841.9%
 
802831.9%
 
602541.7%
 
402521.7%
 
ValueCountFrequency (%) 
0934262.3%
 
103152.1%
 
203022.0%
 
303012.0%
 
402521.7%
 
502992.0%
 
602541.7%
 
702841.9%
 
802831.9%
 
903632.4%
 
ValueCountFrequency (%) 
100300520.0%
 
903632.4%
 
802831.9%
 
702841.9%
 
602541.7%
 
502992.0%
 
402521.7%
 
303012.0%
 
203022.0%
 
103152.1%
 

Interactions

2020-08-24T23:53:26.406697image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:26.562154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:26.711595image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:26.868214image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:27.033884image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:27.187498image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:27.348105image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:27.496851image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:27.645774image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:27.801246image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:27.961353image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:28.121971image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:28.275223image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:28.431523image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:28.576622image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:28.714287image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:28.863575image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:29.008311image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:29.320614image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:29.469977image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:29.607961image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:29.752892image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:29.896591image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:30.042043image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:30.187836image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:30.333105image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:30.476330image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:30.633693image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:30.781108image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:30.942838image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:31.097877image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:31.248543image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:31.407326image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:31.554925image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:31.711781image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:31.867699image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:32.023064image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:32.177384image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:32.331177image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:32.483605image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:32.638569image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:32.787424image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:32.945581image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:33.102605image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:33.260364image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:33.424828image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:33.751935image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:33.901431image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:34.055803image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:34.213896image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:34.370886image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:34.529176image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:34.679341image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:34.838612image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:34.988308image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:35.144736image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:35.296762image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:35.448361image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:35.600103image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:35.743575image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:35.888721image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:36.040232image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:36.204871image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:36.356226image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:36.507130image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:36.650048image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:36.809421image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:36.962269image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:37.120621image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:37.276699image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:37.430527image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:37.590050image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:37.739763image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:37.890440image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:38.228620image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:38.392232image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:38.554246image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:38.707011image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:38.857540image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:39.004795image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:39.143995image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:39.289633image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:39.434432image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:39.579891image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:39.727234image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:39.864492image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:40.003088image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:40.148309image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:40.300080image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:40.450977image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:40.594042image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:40.734730image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:40.882598image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:41.023035image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:41.171509image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:41.322123image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:41.472318image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:41.624506image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:41.763669image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:41.903887image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:42.047210image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:42.229786image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:42.376744image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:42.696557image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:42.836405image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:42.987865image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:43.137015image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:43.295937image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:43.447354image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:43.597067image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:43.755528image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:43.899658image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:44.044482image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:44.204161image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:44.366695image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:44.528910image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:44.686898image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:44.840633image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:44.995193image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:45.157510image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:45.315077image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:45.479375image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:45.637938image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:45.796665image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:45.949442image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:46.101812image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:46.256761image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:46.413175image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:46.573358image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:46.726498image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:46.876402image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:47.207559image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:47.353026image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:47.506266image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:47.665329image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:47.816004image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:47.968684image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:48.116160image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:48.273949image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:48.430499image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:48.586074image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:48.745419image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:48.898873image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:49.048534image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:49.200905image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:49.347246image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:49.499134image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:49.652027image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:49.805717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:49.958237image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:50.108477image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:50.260513image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:50.407705image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:50.569952image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:50.725434image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:50.873490image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:51.016252image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:51.161185image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:51.301619image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:51.632569image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:51.781649image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:51.925271image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:52.072984image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:52.215330image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:52.361728image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:52.508867image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:52.654236image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:52.802014image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:52.943423image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-24T23:53:57.503852image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-24T23:53:57.825833image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-24T23:53:58.147227image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-24T23:53:58.452705image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-24T23:53:53.251209image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:53:53.751246image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

f28f41f27f44f25f38f13f20f5f26f9f4f7f40f34f14f18f46f16target
00.00.00.00.00.00.00.00.060.00.094.0100.076.00.00.00.028.00.00.00.0
10.00.00.00.00.00.00.00.0130.00.094.0100.076.00.00.00.00.00.043.00.0
20.00.00.00.00.00.00.00.0110.00.094.0100.076.00.00.06.050.00.09.00.0
30.00.02.00.00.00.00.00.013.00.094.0100.089.00.00.00.00.00.00.00.0
40.00.00.00.00.00.00.00.015.00.094.0100.078.00.00.00.00.00.01.00.0
560.00.08.00.00.00.00.00.050.00.094.0100.0100.00.00.00.05.00.00.00.0
60.00.00.00.00.00.037.00.0117.00.094.0100.076.00.00.073.00.00.00.00.0
71.00.00.00.00.00.00.043.081.00.094.0100.077.00.00.00.00.00.056.00.0
80.00.00.00.00.00.01.00.044.00.094.0100.076.00.00.01.011.00.03.050.0
90.00.00.00.04.00.00.00.066.00.094.0100.095.00.00.00.00.00.039.070.0

Last rows

f28f41f27f44f25f38f13f20f5f26f9f4f7f40f34f14f18f46f16target
149900.00.00.00.00.00.00.00.0100.00.094.0100.076.00.00.016.022.00.00.020.0
149910.00.00.00.00.00.04.00.021.00.0146.0100.076.00.00.03.01.00.00.00.0
149920.00.00.00.00.00.07.00.084.00.097.0100.076.00.00.02.00.00.029.070.0
149930.00.00.00.00.00.044.05.094.00.094.0100.076.00.00.021.00.00.00.010.0
1499417.00.00.00.00.00.031.00.061.00.094.0100.082.00.00.020.00.00.00.00.0
149950.00.00.00.018.00.02.00.015.023.094.0100.0144.00.00.00.00.00.00.00.0
149960.00.00.00.00.00.00.00.016.00.094.0100.0145.00.00.01.00.00.00.00.0
149970.00.00.00.00.00.017.00.0123.00.094.0100.076.00.00.038.00.00.025.00.0
149980.00.00.00.00.00.03.00.018.00.094.0100.078.00.00.00.00.00.00.00.0
149990.00.00.00.00.00.031.040.0141.00.094.0100.076.00.00.031.00.00.00.00.0

Duplicate rows

Most frequent

f28f41f27f44f25f38f13f20f5f26f9f4f7f40f34f14f18f46f16targetcount
60.00.00.00.00.00.00.00.013.00.094.0100.0130.00.00.00.00.00.00.00.04
260.00.00.00.00.00.00.00.059.00.094.0100.076.00.00.00.00.00.028.0100.04
500.00.00.00.00.00.00.00.074.00.094.0100.076.00.00.00.00.00.021.0100.04
900.00.00.00.00.00.00.00.099.00.094.0100.076.00.00.00.00.00.028.00.04
20.00.00.00.00.00.00.00.013.00.094.0100.0112.00.00.00.00.00.00.00.03
310.00.00.00.00.00.00.00.059.00.094.0100.076.00.00.023.00.00.01.0100.03
350.00.00.00.00.00.00.00.061.00.094.0100.076.00.00.00.00.00.030.0100.03
420.00.00.00.00.00.00.00.071.00.094.0100.078.00.00.00.00.00.037.0100.03
450.00.00.00.00.00.00.00.071.00.095.0100.076.00.00.00.00.00.056.0100.03
470.00.00.00.00.00.00.00.074.00.094.0100.076.00.00.00.00.00.07.0100.03